Replication Data for: Multiple signal classification as a blind reconstruction approach for three-dimensional structured illumination microscopy (doi:10.18710/3POVDT)

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Document Description

Citation

Title:

Replication Data for: Multiple signal classification as a blind reconstruction approach for three-dimensional structured illumination microscopy

Identification Number:

doi:10.18710/3POVDT

Distributor:

DataverseNO

Date of Distribution:

2023-05-31

Version:

1

Bibliographic Citation:

Opstad, Ida, 2023, "Replication Data for: Multiple signal classification as a blind reconstruction approach for three-dimensional structured illumination microscopy", https://doi.org/10.18710/3POVDT, DataverseNO, V1

Study Description

Citation

Title:

Replication Data for: Multiple signal classification as a blind reconstruction approach for three-dimensional structured illumination microscopy

Identification Number:

doi:10.18710/3POVDT

Authoring Entity:

Opstad, Ida (UiT The Arctic University of Norway)

Other identifications and acknowledgements:

Acuña, Sebastian

Other identifications and acknowledgements:

Agarwal, Krishna

Other identifications and acknowledgements:

Birgisdottir, Åsa B.

Other identifications and acknowledgements:

Ahluwalia, Balpreet S.

Other identifications and acknowledgements:

Smart, Katie

Producer:

UiT The Arctic University of Norway

Software used in Production:

SoftWoRx

Software used in Production:

Python

Software used in Production:

ImageJ/Fiji

Grant Number:

Tematiske satsinger (VirtualStain; project number 2061348

Distributor:

DataverseNO

Distributor:

UiT The Arctic University of Norway

Access Authority:

Opstad, Ida

Access Authority:

Agarwal, Krishna

Depositor:

Opstad, Ida Sundvor

Date of Deposit:

2023-05-16

Holdings Information:

https://doi.org/10.18710/3POVDT

Study Scope

Keywords:

Medicine, Health and Life Sciences, super-resolution, 3DSIM, structured illumination microscopy, MUSICAL, multiple signal classification algorithm, mitochondria, kidney, computational nanoscopy

Abstract:

The dataset contains replication data for the research paper "Multiple signal classification as a blind reconstruction approach for three-dimensional structured illumination microscopy" (3DSIM). The paper compares image reconstructions using conventional 3DSIM and two variants of "Multiple signal classification algorithm" (MUSICAL), MUS-S and MUS-CE. <p> This dataset provides 1 image generated of mitochondria in living cells, 1 image of mitochondria in fixed cells, 1 image of nephrin in fixed murine kidney tissue using 3DSIM, MUS-S and MUS-CE. The 3DSIM raw data and reconstructions for H9c2 cells are available from https://doi.org/10.18710/PDCLAS. <p> Four different types of image data are included: -Raw structured illumination kidney data used for all super-resolution image reconstructions (i.e., MUS-S, MUS-CE and 3DSIM) -3DSIM images of a kidney section. -MUS-S for kidney, fixed and live H9c2 cardiomyoblasts -MUS-CE for kidney, fixed and live H9c2 cardiomyoblasts <p> The data is organized in different folders according to sample type (FixedCell/LiveCell H9c2, KidneyTissue), and reconstruction method (MUS-CE, MUS-S, 3DSIM). The image files are TIFF images. <p> Abbreviations:<p> 3DSIM - three-dimensional structured illumination microscopy<p> MUSICAL - Multiple signal classification algorithm<p> MUS-S - soft thresholding variant of MUSICAL<p> MUS-CE - contrast enhancement, a low-resolution variant of MUSICAL<p> LTDR - LysoTracker Deep Red<p> MAX - maximum intensity z-projected three-dimensional images<p> c or ch - channel<p>

Time Period:

2018-06-06-2023-05-05

Date of Collection:

2018-12-12-2020-05-11

Country:

Norway

Geographic Coverage:

Troms, Tromsø

Kind of Data:

super-resolution fluorescence microscopy

Kind of Data:

experimental

Methodology and Processing

Sources Statement

Data Access

Notes:

<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a>

Other Study Description Materials

Related Studies

Opstad, Ida S., 2021, "3DSIM data of mitochondria in the cardiomyoblast cell-line H9c2 adapted to either glucose or galactose", https://doi.org/10.18710/PDCLAS, DataverseNO

Related Publications

Citation

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Submitted for review

Bibliographic Citation:

Submitted for review

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MAX_Kidney_CE1s.tif

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